Find scale changes in a univariate sequence

The uni_scale() function ranks the observations based on their distance from the mean, then applies the pruned exact linear time algorithm with the penalty parameter beta to detect changepoints.

Usage

uni_scale(data, beta = 10)

Arguments

data

A vector or one-dimensional array.

beta

Numeric penalty constant passed to pruned exact linear time algorithm, 10 by default.

Value

A list consisting of:

• $changepoints : Indices of the changepoints detected; will return integer(0) if no changepoints are detected.

• $method : A string "Univariate Changepoint in Scale (KWCChangepoint)"

References

Killick, R., P. Fearnhead, and I. A. Eckley. “Optimal Detection of Changepoints With a Linear Computational Cost.” Journal of the American Statistical Association 107, no. 500 (2012): 1590–98. https://doi.org/10.1080/01621459.2012.737745.

Examples

set.seed(11)
scale_test <- c(rnorm(100, sd=5), # before change in sale
                rnorm(100, sd=1)) # after change in scale
uni_scale(scale_test)
#> $changepoints
#> [1] 100
#> 
#> $method
#> [1] "Univariate Changepoint in Scale (KWCChangepoint)"
#>